Systems Software and Other Abstractions

Unlike memory chips, which have a regular array of elements, processors and logic chips are limited by the rat's nest of wires that span the chip on multiple layers. The bottleneck in logic chip design is not raw numbers of transistors but the lack of a design approach that can use all that capability in a timely fashion. For a solution, several next-generation processor companies have redesigned "systems on silicon" with a distributed computing bent; wiring bottlenecks are localized, and chip designers can be more productive by using a high-level programming language instead of wiring diagrams and logic gates. Chip design benefits from the abstraction hierarchy of computer science.

Compared with the relentless march of Moore's Law, the cognitive capability of humans is relatively fixed. We have relied on the compounding power of our tools to achieve exponential progress. To take advantage of accelerating hardware power, we must further develop layers of abstraction in software to manage the underlying complexity. For the next thousandfold improvement in computing, the imperative will shift to the growth of distributed complex systems. Our inspiration will likely come from biology.

As we race to interpret the now complete map of the human genome and embark upon deciphering the proteome, the accelerating pace of learning is not only opening doors to the better diagnosis and treatment of disease but is also a source of inspiration for much more powerful models of computer programming and complex systems development.

The Biological Muse

Many of the interesting software challenges relate to growing complex systems or have other biological metaphors as inspiration. Some of the interesting areas include Biomimetics, Artificial Evolution, Genetic Algorithms, A-life, Emergence, IBM's Autonomic Computing initiative, Viral Marketing, Mesh, Hives, Neural Networks, and the Subsumption architecture in robotics. The Santa Fe Institute just launched a BioComp research initiative.

In short, biology inspires IT, and IT drives biology. But how inspirational are the information systems of biology? If we took your entire genetic codethe entire biological program that resulted in your cells, organs, body, and mindand burned it into a CD, it would be smaller than Microsoft Office. Just as images and text can be stored digitally, two digital bits can encode the four DNA bases (A, T, C, and G), resulting in a 750MB file that can be compressed for the preponderance of structural filler in the DNA chain.

If, as many scientists believe, most of the human genome consists of vestigial evolutionary remnants that serve no useful purpose, then we could compress it to 60MB of concentrated information. Having recently reinstalled Office, I am humbled by the comparison between its relatively simple capabilities and the wonder of human life. Much of the power in bioprocessing comes from the use of nonlinear fuzzy logic and feedback in the electrical, physical, and chemical domains.

For example, in a fetus, the initial interneuronal connections, or "wiring," of the brain follow chemical gradients. The massive number of interneuron connections in an adult brain could not be simply encoded in our DNA, even if the entire DNA sequence were dedicated to this one task. Your brain has on the order of 100 trillion synaptic connections between 60 billion neurons.

This highly complex system is not "installed," like Microsoft Office, from your DNA. Rather, it is grown, first through widespread connectivity sprouting from "static storms" of positive electrochemical feedback, and then through the pruning of many underused connections through continuous usage-based feedback. In fact, human brains hit their peak at the age of two to three years, with a quadrillion synaptic connections and twice the energy burn of an adult brain.

The brain has already served as an inspirational model for artificial intelligence (AI) programmers. The neural network approach to AI involves the fully interconnected wiring of nodes, followed by the iterative adjustment of the strength of these connections through numerous training exercises and the back-propagation of feedback through the system.

Moving beyond rule-based AI systems, these artificial neural networks are capable of many humanlike tasks, such as speech and visual pattern recognition, with a tolerance for noise

Have you ever been envious of people who seem to have no end of clever ideas, who are able to think quickly in any situation, or who seem to have flawless memories? Could it be that they're just born smarter or quicker than the rest of us? Or are there some secrets that they might know that we don't?